Package: SAMBA 0.9.0

Alexander Rix

SAMBA: Selection and Misclassification Bias Adjustment for Logistic Regression Models

Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. 'SAMBA' implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.

Authors:Alexander Rix [cre], Lauren Beesley [aut]

SAMBA_0.9.0.tar.gz
SAMBA_0.9.0.tar.gz(r-4.5-noble)SAMBA_0.9.0.tar.gz(r-4.4-noble)
SAMBA_0.9.0.tgz(r-4.4-emscripten)SAMBA_0.9.0.tgz(r-4.3-emscripten)
SAMBA.pdf |SAMBA.html✨
SAMBA/json (API)
NEWS

# Install 'SAMBA' in R:
install.packages('SAMBA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • samba.df - Synthetic example data for SAMBA adapted from the vignette

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.18 score 1 packages 154 downloads 97 mentions 5 exports 13 dependencies

Last updated 5 years agofrom:11bcdda5d4. Checks:2 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 29 2025
R-4.5-linuxOKJan 29 2025

Exports:approxdistnonlogisticobsloglikobsloglikEMsensitivity

Dependencies:DBIlatticeMatrixminqamitoolsnloptrnumDerivoptimxpracmaRcppRcppArmadillosurveysurvival

Using SAMBA

Created by Dr. Lauren J Beesley. Contact: [email protected]

Rendered fromUsingSAMBA.Rmdusingknitr::rmarkdownon Jan 29 2025.

Last update: 2020-02-20
Started: 2020-02-20